






'''simple docstring'''







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def       __UpperCamelCase					(      *lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       if not isinstance(lowercase_   ,       lowercase_   ):
              a_						     =			list(lowercase_   )
       for i in range(len(lowercase_   )   ):
              a_						     =			None
       gc.collect()
       if is_xpu_available():
              torch.xpu.empty_cache()
       elif is_npu_available():
              torch.npu.empty_cache()
       else:
              torch.cuda.empty_cache()
       return objects


def       __UpperCamelCase					(      lowercase_       :  Exception   ):
       """simple docstring"""




       a_						     =			[
           'CUDA out of memory.',  # CUDA OOM
           'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.',  # CUDNN SNAFU
           'DefaultCPUAllocator: can\'t allocate memory',  # CPU OOM
       ]
       if isinstance(lowercase_   ,       lowercase_   ) and len(exception.args   ) == 1:
              return any(err in exception.args[0] for err in _statements   )
       return False


def       __UpperCamelCase					(      lowercase_       :  callable = None   ,       lowercase_       :  int = 128   ):
       """simple docstring"""




       if function is None:
              return functools.partial(lowercase_   ,       starting_batch_size=lowercase_   )

       a_						     =			starting_batch_size

       def decorator(*lowercase_       :  str   ,       **lowercase_       :  Dict   ):
              nonlocal batch_size
              gc.collect()
              if is_xpu_available():
                     torch.xpu.empty_cache()
              elif is_npu_available():
                     torch.npu.empty_cache()
              else:
                     torch.cuda.empty_cache()
              a_						     =			list(inspect.signature(lowercase_   ).parameters.keys()   )
              # Guard against user error
              if len(lowercase_   ) < (len(lowercase_   ) + 1):
                     a_						     =			', '.join([F'{arg}={value}' for arg, value in zip(params[1:]   ,       args[1:]   )]   )
                     raise TypeError(
                         F'Batch size was passed into `{function.__name__}` as the first argument when called.'
                         F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'   )
              while True:
                     if batch_size == 0:
                            raise RuntimeError('No executable batch size found, reached zero.'   )
                     try:
                            return function(lowercase_   ,       *lowercase_   ,       **lowercase_   )
                     except Exception as e:
                            if should_reduce_batch_size(lowercase_   ):
                                   gc.collect()
                                   if is_xpu_available():
                                          torch.xpu.empty_cache()
                                   elif is_npu_available():
                                          torch.npu.empty_cache()
                                   else:
                                          torch.cuda.empty_cache()
                                   batch_size //= 2
                            else:
                                   raise

       return decorator












'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase        =							logging.get_logger("transformers.models.speecht5")


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  Dict   ,       lowercase_       :  Any   ):
       """simple docstring"""




       hf_model.apply_weight_norm()

       a_						     =			checkpoint['input_conv.weight_g']
       a_						     =			checkpoint['input_conv.weight_v']
       a_						     =			checkpoint['input_conv.bias']

       for i in range(len(config.upsample_rates   )   ):
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_g']
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_v']
              a_						     =			checkpoint[F'upsamples.{i}.1.bias']

       for i in range(len(config.upsample_rates   ) * len(config.resblock_kernel_sizes   )   ):
              for j in range(len(config.resblock_dilation_sizes   )   ):
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

       a_						     =			checkpoint['output_conv.1.weight_g']
       a_						     =			checkpoint['output_conv.1.weight_v']
       a_						     =			checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  List[str]=None   ,       lowercase_       :  Tuple=None   ,       ):
       """simple docstring"""




       if config_path is not None:
              a_						     =			SpeechTaHifiGanConfig.from_pretrained(lowercase_   )
       else:
              a_						     =			SpeechTaHifiGanConfig()

       a_						     =			SpeechTaHifiGan(lowercase_   )

       a_						     =			torch.load(lowercase_   )
       load_weights(orig_checkpoint['model']['generator']   ,       lowercase_   ,       lowercase_   )

       a_						     =			np.load(lowercase_   )
       a_						     =			stats[0].reshape(-1   )
       a_						     =			stats[1].reshape(-1   )
       a_						     =			torch.from_numpy(lowercase_   ).float()
       a_						     =			torch.from_numpy(lowercase_   ).float()

       model.save_pretrained(lowercase_   )

       if repo_id:
              print('Pushing to the hub...'   )
              model.push_to_hub(lowercase_   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
  parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  __lowerCAmelCase        =							parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )




